Most biomedical research is framed by an outdated view of disease, a linear mind-set that focuses on simple causes rather than complex relationships within dynamic systems. If we are to achieve President Obama's audacious goal of "a cure for cancer in our time," we must radically alter the way we think about biology and disease.

Physicians and medical researchers are traditionally taught to consider disease in terms of simple causes and isolated linear pathways. This one-gene-one-disease approach also informs the way most animal models of disease are developed. Technology readily enables researchers to engineer mice with specific molecular defects in one or a small number of genes as an experimental proxy for human disease. While some of these models are informative and reasonably predictive, most are not.

The limitations of animal models are highlighted by results emerging from powerful genomic studies of human diseases ranging from Type 2 diabetes to pancreatic cancer. For these and many other conditions, the cause is not a single defect, or even a handful of defects, but rather, combinations of hundreds of possible defects, each contributing slightly to the overall risk of disease.

To fully understand complex diseases, and to develop innovative, effective therapies, it is now clear that we must consider not just a single "disease gene" or molecular pathway, but instead to recognize that most diseases result from a disturbance of large interacting networks of molecules. Viewing disease in this context not only provides a more complete description of the underlying biology, it also enables new levels of analysis and insight.

For example, most conventional approaches seek to identify drug targets by looking for a defective link in a chain. All too often, however, drugs developed in this way turn out to be far less effective than anticipated - the molecular networks underlying the disease easily adapt to the drug, minimizing its impact. However, if we adopt a more integrative view of disease etiology, and select therapeutic compounds by assessing their impact on networks and network interactions, then perhaps we can develop more effective drugs and drug combinations that precisely target key vulnerabilities of the disease process, minimize undesirable side effects, and work at comparatively low doses.

One key challenge in developing useful biological networks is the need for better data that reveal associations between DNA mutations and clinical consequence. Properly constructed and securitized electronic medical records would contribute enormously to this effort.

The process of developing useful new networks is also hampered by the sequential nature and long time frame of traditional scientific communication. Missing is the opportunity to effectively harness the aggregate capabilities and creativity of the community of scientists, and to enable real-time coordination and more rapid progress in refining the representations of biology and disease. The success of a range of innovations, from Wikipedia to InnoCentive (originally developed at Eli Lilly) to Procter & Gamble's "Connect and Develop" program, highlight both the power of collective imagination and the need to ensure appropriate monitoring.

Though the field of network biology is only in its infancy, the recent application of network-based approaches to drug discovery has already started to yield results. For instance, more than a third of the metabolism drugs in Merck's current pipeline were derived using this methodology.

The difficulty of transitioning science from a linear to a network mind-set is matched only by the urgent need for success. By both shifting our focus from individual molecules to the complex systems in which these molecules interact, and by developing ways for scientists to share insights earlier and more often, we may achieve real therapeutic breakthroughs that finally provide our patients with the novel, effective medications they have been awaiting for far too long.